def setRegulonFeature(self, request, feature, n):
if feature != "":
if len(request.annotation) > 0:
annotations = [
Annotation(name=ann.name, values=ann.values)
for ann in request.annotation
]
else:
annotations = None
vals, self.cellIndices = self.loom.get_auc_values(
regulon=feature, annotation=annotations, logic=request.logic)
if request.vmax[n] != 0.0:
self.v_max[n] = request.vmax[n]
else:
self.v_max[n], self.max_v_max[
n] = data.get_99_and_100_percentiles(vals)
if request.scaleThresholded:
vals = np.array([
auc if auc >= request.threshold[n] else 0 for auc in vals
])
vals = CellColorByFeatures.normalise_vals(
vals, self.v_max[n], request.vmin[n])
self.features.append(vals)
else:
self.features.append([
constant.UPPER_LIMIT_RGB
if auc >= request.threshold[n] else 0 for auc in vals
])
else:
self.features.append(np.zeros(self.n_cells))
def setGeneFeature(self, request, feature, n):
if feature != "":
if len(request.annotation) > 0:
annotations = [
Annotation(name=ann.name, values=ann.values)
for ann in request.annotation
]
else:
annotations = None
vals, self.cellIndices = self.loom.get_gene_expression(
gene_symbol=feature,
log_transform=request.hasLogTransform,
cpm_normalise=request.hasCpmTransform,
annotation=annotations,
logic=request.logic,
)
if request.vmax[n] != 0.0:
self.v_max[n] = request.vmax[n]
else:
self.v_max[n], self.max_v_max[
n] = data.get_99_and_100_percentiles(vals)
vals = CellColorByFeatures.normalise_vals(vals, self.v_max[n],
request.vmin[n])
self.features.append(vals)
else:
self.features.append(np.zeros(self.n_cells))
def getVmax(self, request, context):
v_max = np.zeros(3)
max_v_max = np.zeros(3)
for n, feature in enumerate(request.feature):
f_v_max = 0
f_max_v_max = 0
if feature != "":
for loomFilePath in request.loomFilePath:
l_v_max = 0
l_max_v_max = 0
loom = self.lfh.get_loom(loom_file_path=Path(loomFilePath))
if request.featureType[n] == "gene":
vals, _ = loom.get_gene_expression(
gene_symbol=feature,
log_transform=request.hasLogTransform,
cpm_normalise=request.hasCpmTransform,
)
l_v_max, l_max_v_max = data.get_99_and_100_percentiles(
vals)
if request.featureType[n] == "regulon":
vals, _ = loom.get_auc_values(regulon=feature)
l_v_max, l_max_v_max = data.get_99_and_100_percentiles(
vals)
if request.featureType[n] == "metric":
vals, _ = loom.get_metric(
metric_name=feature,
log_transform=request.hasLogTransform,
cpm_normalise=request.hasCpmTransform,
)
l_v_max, l_max_v_max = data.get_99_and_100_percentiles(
vals)
if l_v_max > f_v_max:
f_v_max = l_v_max
if l_max_v_max > f_max_v_max:
f_max_v_max = l_max_v_max
v_max[n] = f_v_max
max_v_max[n] = f_max_v_max
return s_pb2.VmaxReply(vmax=v_max, maxVmax=max_v_max)
def update_state(self, step, status_code, status_message, values):
state = GeneSetEnrichment.State(step=step,
status_code=status_code,
status_message=status_message,
values=values)
logger.debug("Status: {0}".format(state.get_status_message()))
if state.get_values() is None:
return s_pb2.GeneSetEnrichmentReply(
progress=s_pb2.Progress(value=state.get_step(),
status=state.get_status_message()),
isDone=False,
cellValues=s_pb2.CellColorByFeaturesReply(color=[],
vmax=[],
maxVmax=[],
cellIndices=[]),
)
else:
vmax = np.zeros(3)
max_vmax = np.zeros(3)
aucs = state.get_values()
_vmax = data.get_99_and_100_percentiles(values=aucs)
vmax[0] = _vmax[0]
max_vmax[0] = _vmax[1]
vals = aucs / vmax[0]
vals = (((constant.UPPER_LIMIT_RGB - constant.LOWER_LIMIT_RGB) *
(vals - min(vals))) /
(1 - min(vals))) + constant.LOWER_LIMIT_RGB
hex_vec = [
"null" if r == g == b == 0 else "{0:02x}{1:02x}{2:02x}".format(
int(r), int(g), int(b)) for r, g, b in zip(
vals, np.zeros(len(aucs)), np.zeros(len(aucs)))
]
cell_indices = list(range(self.loom.get_nb_cells()))
return s_pb2.GeneSetEnrichmentReply(
progress=s_pb2.Progress(value=state.get_step(),
status=state.get_status_message()),
isDone=True,
cellValues=s_pb2.CellColorByFeaturesReply(
color=hex_vec,
vmax=vmax,
maxVmax=max_vmax,
cellIndices=cell_indices),
)
def test_vmax(values):
expected_100 = np.amax(values)
expected_99 = np.clip(np.percentile(values, 99), 0.01, expected_100)
assert data.get_99_and_100_percentiles(values) == (expected_99,
expected_100)